Manufacturers Will Install 15M+ AI-Enabled Devices by 2024, Report Says
September 25, 2019      

SINGAPORE – A new report by ABI Research says the use of artificial intelligence technologies in the industrial manufacturing space is due for a growth spurt over the next five years. The research firm is predicting that the total installed base of AI-enabled devices in industrial manufacturing will reach 15.4 million in 2024, with a compound annual growth rate of 64.8% from 2019 to 2024.

The company defines AI in industrial manufacturing as “a collection of various use cases at different phases of manufacturing, such as generative design in product development, production forecasting in inventory management, machine vision, defect inspection, production optimization, and predictive maintenance in the production phase.” Despite lots of hype about the impact of AI in manufacturing, “the reality is extremely complex”, ABI said. The findings are part of ABI Research’s Industrial AI Market Tracker data report, which is part of the company’s Industrial & Manufacturing research service.

Lian Jye Su, ABI Research

“AI in industrial manufacturing is a story of edge implementation,” said Lian Jye Su, principal analyst at ABI Research. “Since manufacturers are not comfortable having their data transferred to a public cloud, nearly all industrial AI training and inference workloads happen at the edge, namely on device, gateways and on-premise servers.”

AI chipset manufacturers and server vendors have designed AI-enabled servers designed specifically for industrial manufacturing to address this, ABI Research said. This creates more industrial infrastructure with the capability to be equipped with AI software, or dedicated AI chipsets to perform AI inference.

Maintenance, monitoring tops

Within the different use cases of AI in manufacturing, the research firm said predictive maintenance and equipment monitoring was the most commercially implemented so far, “due to the maturity of associated AI models.” In these areas, the installed base is expected to reach 9.8 million (predictive maintenance) and 6.7 million (equipment monitoring) by 2024. ABI noted that many of these AI-enabled industrial devices support multiple use cases on the same device, due to advances in the AI chipsets. Startups in this space, including Uptake, SparkCognition, FogHorn, and Falkonry, are creating cloud- and edge-based solutions that monitor the performance of industrial manufacturing assets and process flows.

A third area that is gaining momentum is defect inspection, the firm added, with a total installed base expected to grow from 300,000 in 2019 to more than 3.7 million by 2024. This use case is extremely popular in electronic and semiconductor manufacturing, where manufacturers such as Samsung, LG, and Foxconn have been partnering with AI chipset vendors and software providers, including CEVA, Gyrfalcon Technology, Lattice Semiconductor, Instrumental, Landing AI, and Neurala, to develop AI-based machine vision that can perform surface, leak, and component-level defect detection, microparticle detection, geometric measurement, and classification.

“Conventional machine vision technology remains popular in the manufacturing factory, due to its proven repeatability, reliability, and stability,” ABI Research said. “However, the emergence of deep learning technology opens the possibility of expanded capabilities and flexibility.” The new algorithms can find unexpected product abnormalities or defects that go beyond existing issues to uncover new insights for manufacturers.

The report also said manufacturers are facing “enormous competition” in building and training in-house data science teams for AI implementation. “Most AI talents prefer to work with webscale giants or AI startups, making talent acquisition a challenging task for industrial manufacturers.”

“As such, they are left with one viable option, which consists of partnering with other players in the AI ecosystem, including cloud service providers, pure-play AI startups, system integrators, chipset and industrial server manufacturers, and connectivity service providers,” said Su. “The diversity in AI use cases necessitates the creation of partnerships.”